Multivariate gaussian process incorporated predictive model for stream turbine power plant

Prama Debnath; Mithun Ghosh

doi:10.30574/gjeta.2022.12.2.0145

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Multivariate gaussian process incorporated predictive model for stream turbine power plant

Prama Debnath ¹ and Mithun Ghosh ^{2, *}

1 Department of Computer Engineering, American International University-Bangladesh, Dhaka, Bangladesh.

2 Department of Systems and Industrial Engineering, University of Arizona, Tucson, Arizona, USA.

Research Article

Global Journal of Engineering and Technology Advances, 2022, 12(02), 096–105.

Article DOI: 10.30574/gjeta.2022.12.2.0145

DOI url: https://doi.org/10.30574/gjeta.2022.12.2.0145

Publication history

Received on 30 December 2021; revised on 18 August 2022; accepted on 22 August 2022

Abstract

Steam power turbine-based power plant approximately contributes 90% of the total electricity produced in the United States. Mainly steam turbine consists of multiple types of turbine, boiler, attemperator, reheater, etc. Power is produced through the steam with high pressure and temperature that is conducted by the turbines. The dynamics of the power plant are highly nonlinear considering all these elements in the model. We proposed to capture the dynamics of the power plant through Simulink modeling where data are generated, and a subsequent data-driven predictive modeling approach is used to detect the power generation from these turbines by the multivariate Gaussian process (MGP). The modeling approach is considered to predict the power generation from these turbines which can capture the cross-correlations between the turbines. Also, the sensitivity analysis of the input parameters is constructed for each turbine to find out the most important factors.

Keywords

Power plant; Steam turbine; Gaussian Process; Cross-correlation relations; Simulation data

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How to cite this article

Prama Debnath and Mithun Ghosh. Multivariate gaussian process incorporated predictive model for stream turbine power plant. Global Journal of Engineering and Technology Advances, 2022, 12(2), 096-105. Article DOI: https://doi.org/10.30574/gjeta.2022.12.2.0145

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